Tislelizumab versus chemotherapy as second-line treatment for European and North American patients with advanced or metastatic esophageal squamous cell carcinoma: a subgroup analysis of the randomized phase III RATIONALE-302 study

BACKGROUND

The phase III RATIONALE-302 study evaluated tislelizumab, an anti-programmed cell death protein 1 antibody, as second-line (2L) treatment for advanced/metastatic esophageal squamous cell carcinoma (ESCC). This prespecified exploratory analysis investigated outcomes in patients from Europe and North America (Europe/North America subgroup).

PATIENTS AND METHODS

Patients with tumor progression during/after first-line systemic treatment were randomized 1 : 1 to open-label tislelizumab or investigator's choice of chemotherapy (paclitaxel, docetaxel, or irinotecan).

RESULTS

The Europe/North America subgroup comprised 108 patients (tislelizumab: n = 55; chemotherapy: n = 53). Overall survival (OS) was prolonged with tislelizumab versus chemotherapy (median: 11.2 versus 6.3 months), with a hazard ratio (HR) of 0.55 [95% confidence interval (CI) 0.35-0.87]; HR was similar irrespective of programmed death-ligand 1 score [≥10%: 0.47 (95% CI 0.18-1.21); <10%: 0.55 (95% CI 0.30-1.01)]. Median progression-free survival was 2.3 versus 2.7 months with tislelizumab versus chemotherapy [HR: 0.97 (95% CI 0.64-1.47)]. Overall response rate was greater with tislelizumab (20.0%) versus chemotherapy (11.3%), with more durable response (median duration of response: 5.1 versus 2.1 months). Tislelizumab had a favorable safety profile versus chemotherapy, with fewer patients experiencing ≥grade 3 treatment-related adverse events (13.0% versus 51.0%). Those on tislelizumab experienced less deterioration in health-related quality of life, physical functioning, and/or disease- and treatment-related symptoms (i.e. fatigue, pain, and eating problems) as compared to those on chemotherapy, per the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (QLQ-C30) and QLQ-OES18 scores.

CONCLUSIONS

As a 2L therapy for advanced/metastatic ESCC, tislelizumab improved OS and had a favorable safety profile as compared to chemotherapy in European/North American ESCC patients in the randomized phase III RATIONALE-302 study.

Machine Learning Differentiation of Autism Spectrum Sub-Classifications

PURPOSE

Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum.

METHODS

We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data.

RESULTS

The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum.

CONCLUSION

Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.

An Artificial Intelligence Derived Blood Test to Diagnose Kawasaki Disease

OBJECTIVE

To develop a highly sensitive and specific blood biomarker panel that identifies febrile children with Kawasaki disease (KD).

METHODS

We tested blood samples from a single-center cohort of KD (n = 50) and control febrile children (n = 100) to develop a biomarker panel from 11 candidates selected by their assay clinical availability. We used machine learning with least absolute shrinkage and selection operator regression to identify 11 blood markers with values incorporated into a model, which provided a binary predictive risk score for KD determined with Youden's index. We further reduced the model using least angle regression.

RESULTS

Using 10-fold cross-validation with least absolute shrinkage and selection operator regression on these 11 readouts plus patient age resulted in an area under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.90-0.98; P <.01). Using Youden's index, which provided an optimal cut off for a binary predictive risk score, 88 of 97 KD-negative patients were diagnosed negative, and 47 of 50 KD-positive patients were positive, yielding a sensitivity of 0.94 (95% CI: 0.87-1.0) and specificity of 0.91 (95% CI: 0.85-0.96). Least angle regression reduced the final panel to 3 biomarkers: C-reactive protein, NT-proB-type natriuretic peptide, and thyroid hormone uptake. The predictive model then provided an area under the receiver operating characteristic curve of 0.92 (95% CI: 0.87-0.96; P <.001) along with sensitivity and specificity at 86% each.

CONCLUSIONS

Machine learning identified a highly accurate diagnostic model for KD. The reduced model employs 3 biomarkers currently approved by regulatory bodies and performed on platforms commonly used by certified diagnostic laboratories.

Performance of a multi-biomarker panel for prediction of cardiovascular event in patients with chronic kidney disease

BACKGROUND

Patients with chronic kidney disease (CKD) undergoing coronary catheterization are at increased risk of cardiovascular events (CVE). Measuring biomarkers before the procedure may guide clinicians in identifying patients at higher risk of future cardiovascular events.

METHODS

In this sub-study the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA), 927 patients underwent coronary catheterization and were followed up for two years. Using machine learning algorithm and targeted proteomics from samples of patients with CKD, 4 biomarkers (kidney injury molecule-1, N-terminal pro B-type natriuretic peptide, osteopontin, and tissue inhibitor of metalloproteinase-1) were integrated into a prognostic algorithm to predict CVE. Results from the panel are expressed in a graded fashion (CVE higher risk and lower risk) using a data-driven cutoff optimized for balanced sensitivity and specificity.

RESULTS

During the 2-year follow-up, 74 CVE were ascertained. 51 (rate: 51/378 = 13.5%) events occurred in stage 1-2 CKD and 23 (rate: 23/68 = 33.8%) events occurred in stage 3-5 CKD. The C-statistic for predicting 2-years cardiovascular events in all 446 patients was 0.77 (0.72, 0.82). The model was well-calibrated (Hosmer-Lemeshow test p-value >0.40). Considering patients at CVE lower-risk within each CKD staging group as a reference, the hazard ratio (95% confidence interval) of cardiovascular events was 2.82 (1.53, 5.22) for CKD stage 1-2/CVE higher-risk, and 8.32 (1.12, 61.76) for CKD stage 3-5/CVE higher-risk.

CONCLUSION

Measuring biomarker panel prior to coronary catheterization may be useful to individualize CVE risk assessment among patients with CKD.

Tislelizumab versus chemotherapy as second-line treatment of advanced or metastatic esophageal squamous cell carcinoma (RATIONALE 302): impact on health-related quality of life

Background

RATIONALE 302 (NCT03430843) an open-label, phase III study of second-line treatment of advanced/metastatic esophageal squamous cell carcinoma (ESCC), reported that tislelizumab, relative to investigator-chosen chemotherapy (ICC), was associated with improvements in overall survival and a favorable safety profile. This study assessed the health-related quality of life (HRQoL) and ESCC-related symptoms of patients in RATIONALE 302.

Methods

Adults with advanced/metastatic ESCC whose disease progressed following prior systemic therapy were randomized 1 : 1 to receive either tislelizumab or ICC (paclitaxel, docetaxel, or irinotecan). HRQoL was measured using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 items (EORTC QLQ-C30), the EORTC Quality of Life Questionnaire Oesophageal Cancer Module 18 items (QLQ-OES18), and the EuroQoL Five-Dimensions Five-Levels (EQ-5D-5L) visual analogue scale. Mixed effect modeling for repeated measurements examined changes from baseline to weeks 12 and 18. The Kaplan–Meier method was used to examine time to deterioration.

Results

Overall, 512 patients were randomized to tislelizumab (n = 256) or ICC (n = 256). The tislelizumab arm maintained QLQ-C30 global health status/quality whereas the ICC arm worsened at week 12 {difference in least square (LS) mean change: 5.8 [95% confidence interval (CI): 2.0-9.5], P = 0.0028} and week 18 [difference in LS mean change: 8.1 (95% CI: 3.4-12.8), P = 0.0008]. Physical functioning (week 18) and fatigue (weeks 12 and 18) worsened less in the tislelizumab compared with the ICC arm. The tislelizumab arm improved in reflux symptoms, whereas the ICC worsened at week 12 [difference in LS mean change: −4.1 (95% CI: −7.6 to −0.6), P = 0.0229]. The visual analogue scale remained consistent in the tislelizumab arm whereas it worsened in the ICC arm. The hazard of time to deterioration was lower in tislelizumab patients compared with ICC for physical functioning and reflux.

Conclusions

HRQoL, including fatigue symptoms and physical functioning, was maintained in patients with advanced or metastatic ESCC receiving tislelizumab compared with ICC-treated patients. These results provide additional support for the benefits of tislelizumab in this patient population.

•

Global health status and HRQoL remained consistent in the tislelizumab arm whereas the ICC arm experienced worsening.

•

Fatigue and physical functioning worsened in both arms; however, the worsening was greater in the ICC arm.

•

The tislelizumab arm was at lower risk of reaching the threshold for worsening in physical functioning and reflux.

The effects of tislelizumab plus chemotherapy as first-line treatment on health-related quality of life of patients with advanced squamous non-small cell lung cancer: Results from a phase 3 randomized clinical trial

BACKGROUND

This study assessed the effects of adding tislelizumab to first-line standard-of- care chemotherapy on the health-related quality of life (HRQoL) of patients with advanced squamous non-small cell lung cancer (sq-NSCLC).

PATIENTS AND METHODS

Patients in this open-label, multicenter, phase 3 RATIONALE 307 trial were randomized to one of the three arms: tislelizumab plus carboplatin and paclitaxel (Arm A), tislelizumab plus carboplatin and nab-paclitaxel (Arm B), or paclitaxel plus carboplatin (Arm C). HRQoL was measured using the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 (QLQ-C30) and the EORTC Quality of Life Questionnaire Lung Cancer 13-item module (QLQ-LC13). Mean score change from baseline at Weeks 6 and 12 in the QLQ-C30's global health status/quality of life (GHS/QoL), fatigue, and physical functioning scores and QLQ-LC13 lung cancer specific subscales were examined. Time to deterioration was estimated for the GHS/QoL score.

RESULTS

A total of 355 sq-NSCLC patients received at least one dose of study drug and completed at least one HRQoL assessment. The GHS/QoL scores improved in Arms A and B relative to Arm C at Weeks 6 and 12. Arms A and B also experienced a reduction in most lung cancer-specific symptoms relative to Arm C. Time to deterioration of GHS/QoL was not reached by any of the three arms.

CONCLUSIONS

The addition of tislelizumab to platinum-based chemotherapy is associated with improvements in sq-NSCLC patients' HRQoL, especially in GHS/QoL and most importantly in lung cancer-specific symptoms including coughing, dyspnea, and hemoptysis.

A pooled multi-national validation study of a machine learning, high-sensitivity troponin-based multi-proteomic model to predict the presence of obstructive coronary artery disease

Background

Undetected obstructive coronary artery disease (oCAD) is a global health problem associated with significant morbidity and mortality. A need exists for an accurate and easily accessible diagnostic test for oCAD. Using machine learning, a multi-biomarker blood diagnostic test for oCAD based on high-sensitivity cardiac troponin-I (hs-cTnI) has been developed.

Purpose

To validate the performance of a previously developed, algorithmically weighted, multiple protein diagnostic panel to diagnose oCAD in a pooled multi-national cohort and to compare the diagnostic panel's performance to predict oCAD to hs-cTnI alone.

Methods

Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. hs-cTnI blood samples were assayed on the Siemens Atellica and Abbott Diagnostics ARCHITECT immunoassay platforms. Adiponectin and Kidney Injury Molecule-1 were measured with a multiplex assay on blood samples via the Luminex 100/200 xMAP platform. Individual data from a total of 924 patients with a mixture of acute and lesser acute presentations from three centers were pooled (Table 1). oCAD was defined as &gt;50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or &gt;70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel's performance to predict oCAD was also compared to hs-cTnI alone.

Results

The multiple protein panel had an area under the receiver-operating characteristic curve of 0.80 (95% CI, 0.77, 0.83, p&lt;0.001) for the presence of oCAD (Figure 1). At optimal cutoff, the score had 74% sensitivity, 72% specificity, and a positive predictive value of 81% for oCAD. The multiple biomarker panel had a diagnostic odds ratio of 7.48 (95% CI 5.55, 10.09, p&lt;0.001). In comparison, in patients without an acute MI, hs-cTnI alone had an area under the receiver-operating characteristic curve of 0.63 (95% CI, 0.60, 0.67, p&lt;0.001)) for oCAD (Figure 1).

Conclusions

In this multinational pooled cohort, a previously described novel machine learning, multiple biomarker panel provided high accuracy to diagnose patients for oCAD.

Funding Acknowledgement

Type of funding sources: Private company. Main funding source(s): Prevencio, Inc. Table 1. Pooled Variable DataFigure 1. ROC for HART CADhs and hs-cTnI

Patient acceptability and usability of a self-administered electronic patient-reported outcome assessment in HIV care: relationship with health behaviors and outcomes

We assessed acceptability/usability of tablet-based patient-reported outcome (PRO) assessments among patients in HIV care, and relationships with health outcomes using a modified Acceptability E-Scale (AES) within a self-administered PRO assessment. Using multivariable linear regression, we measured associations between patient characteristics and continuous combined AES score. Among 786 patients (median age=48; 91% male; 49% white; 17% Spanish-speaking) overall mean score was 26/30 points (SD: 4.4). Mean scores per dimension (max 5, 1=lowest acceptability, 5=highest): ease of use 4.7, understandability 4.7, time burden 4.3, overall satisfaction 4.3, helpfulness describing symptoms/behaviors 4.2, and enjoyability 3.8. Higher overall score was associated with race/ethnicity (+1.3 points/African-American patients (95%CI:0.3-2.3); +1.6 points/Latino patients (95%CI:0.9-2.3) compared to white patients). Patients completing PROs in Spanish scored +2.4 points on average (95%CI:1.6-3.3). Higher acceptability was associated with better quality of life (0.3 points (95%CI:0.2-0.5)) and adherence (0.4 points (95%CI:0.2-0.6)). Lower acceptability was associated with: higher depression symptoms (-0.9 points (95%CI:-1.4 to -0.4)); recent illicit opioid use (-2.0 points (95%CI:-3.9 to -0.2)); multiple recent sex partners (-0.8 points (95%CI:-1.5 to -0.1)). While patients endorsing depression symptoms, recent opioid use, condomless sex, or multiple sex partners found PROs less acceptable, overall, patients found the assessments highly acceptable and easy to use.

Diffusion tensor cardiovascular magnetic resonance detects altered myocardial microstructure in patients with acute st-elevation myocardial infarction

Background

Diffusion Tensor Cardiovascular Magnetic Resonance (DT-CMR) can quantify metrics of tissue integrity (mean diffusivity [MD] and fractional anisotropy [FA]) and changes in laminar microstructures (sheetlets), which reorientate from more wall-parallel in diastole (DIA) towards wall-perpendicular in systole (SYS) as the myocardium thickens, quantified by E2 angle [E2A]. Microstructural changes after STEMI may provide new insights into adverse LV remodelling and risk stratification.

Methods

In vivo DT-CMR was performed 3–5 days after PPCI for first presentation STEMI (N=19, mean age 57±9, 79% male). DT-CMR was acquired in 2 short-axes (SYS & DIA) using a STEAM-EPI sequence. 12 segment analysis of MD, FA, E2A and E2A mobility (ΔE2A = E2ASYS − E2ADIA) was performed. Infarct (INF) segments were defined as &gt;25% LGE, adjacent (ADJ, located contiguous to INF) and remote (REM, all other segments). Wilcoxon signed rank tests were used with threshold P&lt;0.017 (Bonferroni corrected).

Results

See Table.

MD in both SYS and DIA was significantly higher in INF and ADJ regions compared to REM. FA in both SYS and DIA was lower in the INF and ADJ compared to REM. E2ADIA was higher in INF, indicating a more wall-perpendicular orientation of sheetlets, compared to ADJ and REM zones. E2ASYS in INF was significantly reduced, indicating a more wall-parallel orientation of sheetlets, compared to ADJ and REM regions, resulting in significantly reduced sheetlet mobility (ΔE2A).

Conclusions

Microstructural changes can be detected after acute STEMI by in vivo DT-CMR. Zonal changes in MD and FA may suggest loss of barriers to water diffusion and altered cardiomyocyte organisation, respectively. We provide the first report of reduced sheetlet mobility after acute STEMI in INF. Ongoing work is evaluating the mechanisms and prognostic importance of altered sheetlet mobility after STEMI.

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): British Heart Foundation Clinical Research Training Fellowship

Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease

Background

Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs‐cTn (high‐sensitivity cardiac troponin)–based proteomic model to diagnose obstructive coronary artery disease.

Methods and Results

In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs‐cTnI [high‐sensitivity cardiac troponin I], adiponectin, and kidney injury molecule‐1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% (P<0.001). At the optimal cutoff, we observed 80% sensitivity, 71% specificity, a positive predictive value of 83%, and negative predictive value of 66% for ≥70% stenosis. Partitioning the score result into 5 levels resulted in a positive predictive value of 97% and a negative predictive value of 89% at the highest and lowest levels, respectively. In the external validation cohort, the score performed similarly well. Notably, in patients who had myocardial infarction neither ruled in nor ruled out via hs‐cTnI testing (“indeterminate zone,” n=65), the score had an area under the receiver operating characteristic curve of 0.88 (P<0.001).

Conclusions

A model including hs‐cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs‐cTnI concentrations.

Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction

Background: In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). Methodology & results: A previously described prognostic panel for MACE consisting of four biomarkers was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. Conclusion: Among patients with suspected MI, we validated a multibiomarker panel for predicting 1-year MACE. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov).

Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus

Background

Patients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM.

Methods and results

The HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up.

Conclusion

A clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.

Trial registration number

NCT00842868.

Multiple biomarker panel to screen for severe aortic stenosis: results from the CASABLANCA study

Objective

Severe aortic valve stenosis (AS) develops via insidious processes and can be challenging to correctly diagnose. We sought to develop a circulating biomarker panel to identify patients with severe AS.

Methods

We enrolled study participants undergoing coronary or peripheral angiography for a variety of cardiovascular diseases at a single academic medical centre. A panel of 109 proteins were measured in blood obtained at the time of the procedure. Statistical learning methods were used to identify biomarkers and clinical parameters that associate with severe AS. A diagnostic model incorporating clinical and biomarker results was developed and evaluated using Monte Carlo cross-validation.

Results

Of 1244 subjects (age 66.4±11.5  years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) <1.0  cm²). A final model included age, N-terminal pro-B-type natriuretic peptide, von Willebrand factor and fetuin-A. The model had good discrimination for severe AS (OR=5.9, 95% CI 3.5 to 10.1, p<0.001) with an area under the curve of 0.76 insample and 0.74 with cross-validation. A diagnostic score was generated. Higher prevalence of severe AS was noted in those with higher scores, such that 1.6% of those with a score of 1 had severe AS compared with 15.3% with a score of 5 (p<0.001), and score values were inversely correlated with AVA (r=−0.35; p<0.001). At optimal model cut-off, we found 76% sensitivity, 65% specificity, 13% positive predictive value and 98% negative predictive value.

Conclusions

We describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS.

Trial registration number

NCT00842868.

A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study

BACKGROUND

Peripheral arterial disease (PAD) is a global health problem that is frequently underdiagnosed and undertreated. Noninvasive tools to predict the presence and severity of PAD have limitations including inaccuracy, cost, or need for intravenous contrast and ionizing radiation.

HYPOTHESIS

A clinical/biomarker score may offer an attractive alternative diagnostic method for PAD.

METHODS

In a prospective cohort of 354 patients referred for diagnostic peripheral and/or coronary angiography, predictors of ≥50% stenosis in ≥1 peripheral vessel (carotid/subclavian, renal, or lower extremity arteries) were identified from >50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed.

RESULTS

The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1, and eotaxin-1). The model had an in-sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross-validated area under the curve of 0.84; higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 65% sensitivity, 88% specificity, 76% positive predictive value (PPV), and 81% negative predictive value (NPV) for obstructive PAD and performed consistently across vascular territories. Partitioning the score into 5 levels resulted in a PPV of 86% and NPV of 98% in the highest and lowest levels, respectively. Elevated score was associated with shorter time to revascularization during 4.3 years of follow-up.

CONCLUSIONS

A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.

A Fixed-point Scheme for the Numerical Construction of Magnetohydrostatic Atmospheres in Three Dimensions

Magnetohydrostatic models of the solar atmosphere are often based on idealized analytic solutions because the underlying equations are too difficult to solve in full generality. Numerical approaches, too, are often limited in scope and have tended to focus on the two-dimensional problem. In this article we develop a numerical method for solving the nonlinear magnetohydrostatic equations in three dimensions. Our method is a fixed-point iteration scheme that extends the method of Grad and Rubin (Proc. 2nd Int. Conf. on Peaceful Uses of Atomic Energy31, 190, 1958) to include a finite gravity force. We apply the method to a test case to demonstrate the method in general and our implementation in code in particular.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

Validation of urinary HE4 as a biomarker for ovarian cancer

5563

Background: Urinary HE4 has been reported as a promising biomarker for ovarian cancer (OC). In the current study, a large validation was performed to evaluate urinary HE4 as a biomarker for the stratification of OC from benign pelvic masses. Methods: Normalized HE4 was obtained from the ratio of HE4 (pmol/L measured with HE4 EIA) and creatinine (mg/dL measured with a Jaffe Reaction) in single-point urine samples from female subjects with a pelvic mass (N = 809). The samples were from one case-control prospective study (Moore RG, 2008) and two prospective clinical trials (NCT00315692 and NCT00987649). R Package was used to randomly distribute the subjects into the Training and Testing Sets. Analyze-it was used to analyze the clinical performance. Results: Medians of Normalized HE4, sensitivities (SN) and likelihood ratios (LR) (+) of Normalized HE4 at two levels of specificity (SP) are presented in the Table. Conclusions: Urinary HE4 normalized with urinary creatinine appears to be a valid biomarker for the stratification of OC from a benign pelvic mass. [Table: see text]

Dynamic causal modelling of lateral interactions in the visual cortex

This paper presents a dynamic causal model based upon neural field models of the Amari type. We consider the application of these models to non-invasive data, with a special focus on the mapping from source activity on the cortical surface to a single channel. We introduce a neural field model based upon the canonical microcircuit (CMC), in which neuronal populations are assigned to different cortical layers. We show that DCM can disambiguate between alternative (neural mass and field) models of cortical activity. However, unlike neural mass models, DCM with neural fields can address questions about neuronal microcircuitry and lateral interactions. This is because they are equipped with interlaminar connections and horizontal intra-laminar connections that are patchy in nature. These horizontal or lateral connections can be regarded as connecting macrocolumns with similar feature selectivity. Crucially, the spatial parameters governing horizontal connectivity determine the separation (width) of cortical macrocolumns. Thus we can estimate the width of macro columns, using non-invasive electromagnetic signals. We illustrate this estimation using dynamic causal models of steady-state or ongoing spectral activity measured using magnetoencephalography (MEG) in human visual cortex. Specifically, we revisit the hypothesis that the size of a macrocolumn is a key determinant of neuronal dynamics, particularly the peak gamma frequency. We are able to show a correlation, over subjects, between columnar size and peak gamma frequency — that fits comfortably with established correlations between peak gamma frequency and the size of visual cortex defined retinotopically. We also considered cortical excitability and assessed its relative influence on observed gamma activity. This example highlights the potential utility of dynamic causal modelling and neural fields in providing quantitative characterisations of spatially extended dynamics on the cortical surface — that are parameterised in terms of horizontal connections, implicit in the cortical micro-architecture and its synaptic parameters.

SU-E-I-53: Optimal KVp for Image Quality and Noise in Iodine Contrast in Head CTA

PURPOSE

For brain CT perfusion it is well established that 80 kVp is optimal. Although neuro-CT angiography is somewhat similar, emphasis is on the detection of aneurysms and related vascular pathologies throughout the brain. Thus it is necessary to visualize small and large blood vessels with contrast material, as well as form multi-planar views and 3D images, so image quality and noise in addition to contrast are important for thin slices. A study was initiated to determine the optimal kVp for neuro-CTA.

METHODS

A customized version of a commercial head phantom (CIRS 007TE-27 medium adult head CT dose phantom) was purchased to facilitate quantitative measurements with iodinated contrast material, contrast for white and gray matter, and to maintain the ability to perform dosimetry. The customization consisted of adding four 25 mm holes, 35 mm from the center arranged at 45 degree angles from the center, with solid rods equivalent with brain, white, and gray matter, as well as four fillable vials were included for study of contrast agents. Dosimetry measurements were carried out with standard pencil chamber and with 0.6 cc ionization chamber. For study of the optimal kVp for a head CTA, the vials were filled with four different concentrations of contrast, approximating low to medium concentrations that would be expected in such a study. The standard CTA protocol was followed, 64 × 0.625, pitch 0.53, rotation speed 0.5 second, and CTDIvol was kept constant for each kVp.

RESULTS

The best contrast was observed at 80 kVp; however, in order to achieve noise in CTA low enough to be clinically useful there may be issues with tube current capability for a clinical technique. Clinical investigation is underway.

CONCLUSIONS

The best balance of contrast and noise currently possible will be achieved at 100 kVp in a clinical scan.

Abstract LB-451: Detection of urinary HE4 as a biomarker for epithelial ovarian cancer

Human epididymis protein 4 (HE4) has been shown to be a promising serum biomarker for epithelial ovarian cancer (EOC). Recently, Hellstrom et al. reported the detection of HE4 in urine as a biomarker for ovarian neoplasms (Cancer Lett.2010;296:43–8). We investigated the feasibility of urinary HE4 in detection of EOC in a pilot study. METHODS: This retrospective study measured the HE4 levels in urine samples obtained from an institutional review board-approved sample bank. HE4 was measured with HE4 EIA (Fujirebio Diagnostics, Inc.). Creatinine was measured via colorimetric determination by the Jaffe Reaction and used to normalize the HE4 levels. Clinical diagnosis of the subjects was based on the pathological diagnosis. Single-point urine samples were analyzed from 232 subjects; 72 with EOC or low malignant potential tumors and 160 with benign disease. RESULTS: The medians of HE4 levels in subjects with EOC and with benign disease were 29833 pmol/L (95% Confidence Interval (CI): 21185–54728) and 8515 pmol/L (95% CI: 7077–9314) respectively. The medians of HE4/Creatinine ratios in subjects with EOC and with benign disease were 420 (95% CI: 269–491) and 75 (95% CI: 68–83), respectively. Both the medians of HE4 levels and HE4/Creatinine ratios in EOC subjects were significantly higher than that in subjects with benign disease (P&lt;0.0001 in Median test). Receiver operator characteristic (ROC) analysis showed that areas under the curve (AUC) were 0.87 for HE4/Creatinine ratio (95% CI = 0.81–0.92) and 0.83 for HE4 level (95% CI = 0.76–0.89). There was no significant difference between the AUC for HE4 level and HE4/Creatinine ratio (P&gt;0.05). Using the shoulder point of each ROC curve as the cut-off, the cut-off values were 152.5 for HE4/Creatinine ratio and 19161 pmol/L for HE4 level respectively. The sensitivity, specificity, positive predictive value and negative predictive value at the set cut-off values were 70.8% (95% CI = 58.9% – 81.0%), 93.1% (95% CI = 88.0% – 96.5%), 82.3% and 87.6% for HE4/Creatinine ratio, and 65.3% (95% CI = 53.1% – 76.1%), 89.4% (95% CI = 83.5% – 93.7%), 73.4% and 85.1% for HE4 level, respectively. Concordance rates versus clinical diagnosis were 86.2% for HE4/creatinine ratio and 81.9% for HE4 level. CONCLUSIONS: This pilot study demonstrated the utility of urinary HE4 level alone or HE4/Creatinine ratio as potential biomarkers for the detection of EOC. The HE4/Creatinine ratio appeared to be better than the HE4 level in correlating with the clinical pathological diagnosis of EOC.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-451. doi:10.1158/1538-7445.AM2011-LB-451

Abstract 4653: Role of serum HE4 in studying ovarian cancer progression and response to therapy with ARCHITECT HE4 assay

Human epididymis protein 4 (HE4) is a whey acidic four-disulfide core protein. Serum HE4 has been shown to be a promising biomarker for epithelial ovarian cancer (EOC) with manual HE4 EIA assays. The ARCHITECT system is one of the major automated formats for cancer immunodiagnostics. This abstract describes the clinical performance of an investigational ARCHITECT HE4 assay in the measurement of serum HE4 from longitudinal samples of subjects with EOC and single-point samples from healthy subjects, and subjects with malignancy, benign diseases, EOC or other malignant diseases.

PROCEDURES: The ARCHITECT HE4 Assay is a paramagnetic microparticle chemiluminescent assay adopting the sensitive CHEMIFLEX® technology. Single-point and longitudinal serum samples were tested with an investigational ARCHITECT HE4 Assay reagent lot on the ARCHITECT i2000SR. Single-point sera were collected from healthy subjects (N = 400), subjects with pregnancy (N = 50) and benign disease (N = 612), and subjects which were diagnosed with EOC (N = 314) and other cancers (N = 250) including endometrial, breast, gastrointestinal, lung, and bladder cancer. Longitudinal sera (N = 506), including initial time point and follow-up visits (average = 5.7 per subject), were collected from subjects (N = 76) which were diagnosed with EOC. All of the single-point and longitudinal samples were handled in accordance with IRB-approved and GCP-compliant protocols.

RESULTS: Using an ARCHITECT HE4 cut-off value of &gt;140 pmol/L, the percent distributions of ARCHITECT HE4 by cohort are: 3% of healthy subjects, 0% with pregnancy, 7.2% of subjects with benign diseases, 53.5% with EOC and 28.4% with other cancers. Using the upper 95% point of the distribution of the HE4 ratio at 14% to categorize the successive HE4 readings into those that were and were not significantly elevated for the longitudinal samples, the sensitivity, specificity and positive predictive value of HE4 elevation versus disease progression were 53.4% (95% CI: 44.1%-62.5%), 78.8% (95% CI: 74.7% - 82.4%) and 43.2% (95% CI: 32.9% - 54.1%) respectively. The total concordance between HE4 elevation and disease progression was 73%. Receiver Operator Characteristic analysis showed an area under the curve of 0.685 (SE = 0.033) for the diagnosis of progression from the ratio of successive HE4 readings. When comparing the change of the HE4 ratio with the diagnostic response to therapy, a decrease in HE4 reflected response to therapy with a sensitivity of 82% and a specificity of 53%.

CONCLUSIONS: The study with single-point samples has shown the utility of ARCHITECT HE4 values as a serum biomarker for EOC. The longitudinal increase of ARCHITECT HE4 values in sera seemed to be effective in recognizing the progression of EOC. The longitudinal decrease of ARCHITECT HE4 values in sera seemed to correlate with the response of EOC subjects to therapy.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4653.

Acute cardiovascular effects of apelin in humans: potential role in patients with chronic heart failure

BACKGROUND

Apelin, the endogenous ligand for the novel G protein-coupled receptor APJ, has major cardiovascular effects in preclinical models. The study objectives were to establish the effects of acute apelin administration on peripheral, cardiac, and systemic hemodynamic variables in healthy volunteers and patients with heart failure.

METHODS AND RESULTS

Eighteen patients with New York Heart Association class II to III chronic heart failure, 6 patients undergoing diagnostic coronary angiography, and 26 healthy volunteers participated in a series of randomized, double-blind, placebo-controlled studies. Measurements of forearm blood flow, coronary blood flow, left ventricular pressure, and cardiac output were made by venous occlusion plethysmography, Doppler flow wire and quantitative coronary angiography, pressure wire, and thoracic bioimpedance, respectively. Intrabrachial infusions of (Pyr(1))apelin-13, acetylcholine, and sodium nitroprusside caused forearm vasodilatation in patients and control subjects (all P<0.0001). Vasodilatation to acetylcholine (P=0.01) but not apelin (P=0.3) or sodium nitroprusside (P=0.9) was attenuated in patients with heart failure. Intracoronary bolus of apelin-36 increased coronary blood flow and the maximum rate of rise in left ventricular pressure and reduced peak and end-diastolic left ventricular pressures (all P<0.05). Systemic infusions of (Pyr(1))apelin-13 (30 to 300 nmol/min) increased cardiac index and lowered mean arterial pressure and peripheral vascular resistance in patients and healthy control subjects (all P<0.01) but increased heart rate only in control subjects (P<0.01).

CONCLUSIONS

Acute apelin administration in humans causes peripheral and coronary vasodilatation and increases cardiac output. APJ agonism represents a novel potential therapeutic target for patients with heart failure.